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https://hdl.handle.net/1822/79446
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Campo DC | Valor | Idioma |
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dc.contributor.author | Oliveira, Pedro | por |
dc.contributor.author | Fernandes, B. | por |
dc.contributor.author | Aguiar, Francisco | por |
dc.contributor.author | Pereira, M. A. | por |
dc.contributor.author | Novais, Paulo | por |
dc.date.accessioned | 2022-09-07T16:09:45Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Oliveira, P., Fernandes, B., Aguiar, F., Pereira, M.A., Novais, P. (2021). Evaluating Unidimensional Convolutional Neural Networks to Forecast the Influent pH of Wastewater Treatment Plants. In: , et al. Intelligent Data Engineering and Automated Learning – IDEAL 2021. IDEAL 2021. Lecture Notes in Computer Science(), vol 13113. Springer, Cham. https://doi.org/10.1007/978-3-030-91608-4_44 | por |
dc.identifier.isbn | 978-3-030-91607-7 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | https://hdl.handle.net/1822/79446 | - |
dc.description.abstract | One of our society’s challenges today is water resources management due to its importance for human life. The monitoring of various substances present in wastewater is a crucial part of the process of Wastewater Treatment Plants (WWTPs). One of these substances is the influent’s pH, which plays a fundamental role in the nitrification and nitration processes. Hence, this paper presents a study to forecast the influent pH in a WWTP for the next two days. For this purpose, several candidate models were conceived, tunned and evaluated, taking into account the one-dimensional Convolutional Neural Networks (CNNs) considering two distinct approaches in the Pooling layer: the channels’ last and the channels’ first. The best candidate model obtained a Mean Absolute Error (MAE) of 0.257, following the channel’s last approach, compared to the channels’ first that obtained a MAE of 0.272. | por |
dc.description.sponsorship | This work is financed by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia within project DSAIPA/AI/0099/2019. | por |
dc.language.iso | eng | por |
dc.publisher | Springer, Cham | por |
dc.relation | info:eu-repo/grantAgreement/FCT/3599-PPCDT/DSAIPA%2FAI%2F0099%2F2019/PT | por |
dc.rights | restrictedAccess | por |
dc.subject | Convolutional Neural Networks | por |
dc.subject | Deep Learning | por |
dc.subject | Influent pH | por |
dc.subject | Time series | por |
dc.subject | Wastewater Treatment Plants | por |
dc.title | Evaluating unidimensional convolutional neural networks to forecast the influent pH of wastewater treatment plants | por |
dc.type | conferencePaper | por |
dc.peerreviewed | yes | por |
dc.relation.publisherversion | https://link.springer.com/chapter/10.1007/978-3-030-91608-4_44 | por |
oaire.citationStartPage | 446 | por |
oaire.citationEndPage | 457 | por |
oaire.citationVolume | 13113 LNCS | por |
dc.date.updated | 2022-08-30T19:27:26Z | - |
dc.identifier.doi | 10.1007/978-3-030-91608-4_44 | por |
dc.date.embargo | 10000-01-01 | - |
dc.identifier.eisbn | 978-3-030-91608-4 | - |
dc.subject.fos | Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática | por |
sdum.export.identifier | 11142 | - |
sdum.journal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | por |
oaire.version | AM | por |
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